Research On Text Detection In Images And Video Frames

Text in images and video frames carries important information for visual content understanding and video retrieval. Different from traditional patterns (single character, humance face, etc.), text line varies in size, grey, shape and color. Furthermore, some texe line embedded in complex background. These bring on difficulties to text detetection and recognition. Traditional learning-based classification method on image block is unsutibale for text detection. While in this thesis, a general framework is proposed for text detection based on coarse-to-fine object detection idea. We justify the effectiveness and importance of the framework in three kinds of representative text patterns including (1) video overlay text (2) text in natural scene images (3) single digital character. In the text detection process, text will be located by its most identical feature and then verified by other effective features. This scheme will own fast detection speed and high precision. During the summarization and exention, we discuss the feasibility of applying the coarse-to-fine method on other texture object detection task.For the overlay text in video frames, multiscale wavelet features are extracted for text detection. Feature combination and selection for text/nontext discrimination are emphasized in this algorithm. Firstly, in the coarse detection, after the wavelet energy feature is calculated to locate all possible text pixels, a density-based region growing method is developed to connect these pixels into regions which are further separated into candidate text lines by structural information. Secondly, in the fine detection, with three kinds of texture features and one kind of structure feature extracted to represent the texture pattern of a text line, a forward search algorithm is applied to select the most effective features. Finally, an SVM classifier is used to identify true text from the candidates based on the selected features. Experimental results show that this approach can fast and robustly detect text lines under various conditions.Text often lies in complex background and then bad recognition result is reported on grey-value based OCR software. We proposed an automatic method to segment text from complex background for recognition task. A rule-based sampling method is proposed to get portion of the text pixels. Then, the sampled pixels are used for training Gaussian Mixture Models of intensity and hue components in HSI color space. Finally, the trained GMMs together with the spatial connectivity information are used for segment all of text pixels form their background. Experiments results show that the proposed algorithm can work fully automatically and performs much better than the traditional methods.In the scene text location algorithm, color, wavelet histogram and OCR feedback features are combined in different detecting stages. Image pixels firstly are grouped into regions by...